Scientists and radiologists may want to design and use a magnetic resonance imaging (MRI) pulse sequence that will produce a signal evolution having desired signal characteristics. The pulse sequence may have user controllable magnetic resonance (MR) settings including, for example, acquisition period (AP), or flip angle (FA). Conventional manual approaches to designing new sequences have been complex and time consuming. Conventional manual approaches may have produced a pulse sequence with a constant AP and FA. While this type of pulse sequence may have been useful for some applications, it may have had limited, if any, value in magnetic resonance fingerprinting.
An artificial neural network (ANN) has been used to facilitate automatically designing some new sequences that produce signals having desired signal characteristics. For example, using an ANN to predict MR sequence parameters for a desired signal output was demonstrated in N Geshnizjani et al, Conference Abstract: ISMRM 2013, 2380, and H Marschner at al, Conference Abstract: ISMRM 2013, 4239.
Conventional approaches apply known pulse sequences with known signal characteristics to train an ANN. However, these conventional approaches may have been constrained with respect to acceptable initial magnetizations and thus with respect to the range of signal evolutions that could be produced. While previous work has taken a first step in automating sequence design, improved approaches would facilitate producing signal evolutions with a range sufficient to support magnetic resonance fingerprinting.
Magnetic resonance fingerprinting (MRF) employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which radio frequency (RF) energy is applied. MRF sequence blocks may vary widely, either non-linearly, randomly, and/or pseudo-randomly. Since the sequence blocks may vary widely, the resulting signal evolutions may also vary widely. Sequence blocks may vary in a number of parameters including, but not limited to, echo time, flip angle, phase encoding, diffusion encoding, flow encoding, RF pulse amplitude, RF pulse phase, number of RF pulses, type of gradient applied between an excitation portion of a sequence block and a readout portion of a sequence block, number of gradients applied between an excitation portion of a sequence block and a readout portion of a sequence block, type of gradient applied between a readout portion of a sequence block and an excitation portion of a sequence block, number of gradients applied between a readout portion of a sequence block and an excitation portion of a sequence block, type of gradient applied during a readout portion of a sequence block, number of gradients applied during a readout portion of a sequence block, amount of RF spoiling, or amount of gradient spoiling. In different embodiments two, three, four, or more parameters may vary between sequence blocks. In different embodiments, the number of parameters varied between sequence blocks may itself vary. For example, A1 (sequence block 1) may differ from A2 in five parameters, A2 may differ from A3 in seven parameters, A3 may differ from A4 in two parameters, and so on. One skilled in the art will appreciate that there are a nearly infinite number of series of sequence blocks that can be created by varying this large number of parameters.
Conventional ANN approaches may have facilitated producing a pulse sequence having fixed parameters. Designing conventional pulse sequences that have constant sequence blocks and fixed parameters involves a first level of complexity. Designing MRF pulse sequences that have varied sequence blocks suitable for MRF involves a second higher level of complexity.
The term “resonant species”, as used herein, refers to an item (e.g., water, fat, tissue, material) that can be made to resonate using nuclear magnetic resonance (NMR). By way of illustration, when RF energy is applied to a volume that has bone and muscle tissue, then both the bone and muscle tissue will produce an NMR signal. However the “bone signal” and the “muscle signal” will be different and can be distinguished using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions or previously acquired evolutions. A large set of known evolutions may be stored in a dictionary.
Characterizing the resonant species can include identifying different properties of a resonant species (e.g., T1, T2, diffusion resonant frequency, diffusion co-efficient, spin density, proton density). Additionally, other properties including, but not limited to, tissue types, materials, and super-position of attributes can be identified. These properties may be identified simultaneously using MRF, which is described in U.S. Pat. No. 8,723,518 “Nuclear Magnetic Resonance (NMR) Fingerprinting” and in Magnetic Resonance Fingerprinting, Ma et al., Nature 495, 187-192 (14 Mar. 2013), the contents of both of which are incorporated herein by reference.